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Predicting product purchases from transaction data using aspect models

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dc.contributor.advisor Dr. Peter Haddawy (Chairman) en_US
dc.contributor.advisor Dr. Vatcharaporn Esichaikul (Member) en_US
dc.contributor.advisor Dr. Sumanta Guha (Member) en_US
dc.contributor.author Ha Hong Thuy en_US
dc.date.accessioned 2015-01-12T10:44:59Z
dc.date.available 2015-01-12T10:44:59Z
dc.date.issued 2003 en_US
dc.identifier.other AIT Thesis no.IM-03-10 en_US
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/550
dc.description 55 p. en_US
dc.description.abstract Predicting product purchases is one of the important tasks of a broker in the barter trade exchange. This work introduces the utilization of aspect models - a latent class statistical mixture model used for soft-clustering of co-occurrence data - for generating future purchase predictions for existing and new members in a barter exchange from transaction data. Three aspect models are investigated. Expectation Maximization (EM) algorithm and Annealed Expectation Maximization algorithm are used to fit the models with the data. A system is implemented to train and evaluate the performance of the proposed models and algorithms. Several experiments are carried out to determine the optimal number of states for aspects for each model and to determine which model performs better. The experimental results show that aspect models work well in predicting product purchases from transaction data. en_US
dc.description.sponsorship Vietnamese Ministry of Education and Training en_US
dc.language.iso en en_US
dc.publisher Asian Institute of Technology en_US
dc.relation.ispartofseries AIT Publications; en_US
dc.subject Aspect models en_US
dc.subject Purchases en_US
dc.title Predicting product purchases from transaction data using aspect models en_US
dc.type Thesis en_US

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